2021 IEEE International Systems Conference (SysCon)最新文献

A Model Based Neurorehabilitation (MBN) Framework using Kinect 基于Kinect的模型神经康复(MBN)框架

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447118

Zaeem Anwaar, M. Rashid, F. Azam, Muhammad Waseem Anwar, Yawar Rasheed, Maryum Humdani

{"title":"A Model Based Neurorehabilitation (MBN) Framework using Kinect","authors":"Zaeem Anwaar, M. Rashid, F. Azam, Muhammad Waseem Anwar, Yawar Rasheed, Maryum Humdani","doi":"10.1109/SysCon48628.2021.9447118","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9447118","url":null,"abstract":"Neurological disorders are frequently reported across the world. Patients affected with neurological disorders requires a definite rehabilitation. Various speech and auditory rehabilitation systems have been developed over the period and reported in literature as well. However, modern technological trends require prompt development of complex systems with simplicity. Model Driven Architecture (MDA) has served the purpose for variety of domains and the area of Neurorehabilitation also requires exploration in the context of MDA. This article introduces an MBN (Model-Based Neurorehabilitation) framework. It consists of a meta-model, tree editor, Sirius based graphical modeling tool with drag and drop palette and a model to text transformation engine that transforms the modeled scenario into java code. The framework has the capability to assign various activities like physiotherapy sessions for patients having neuro disorders using Kinect Sensor V2. Currently, our Sirius based graphical modeling tool allows modeling and visualization of various activities that are assigned to patients in order to overcome a specific disorder. The validity of proposed framework is demonstrated via a case study The results from the case study prove that our framework is very effective and capable of modeling and visualizing activities successfully.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116732519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Anomaly Detection Technique for Intrusion Detection in SDN Environment using Continuous Data Stream Machine Learning Algorithms 基于连续数据流机器学习算法的SDN入侵异常检测技术

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447092

A. Ribeiro, R. Santos, A. Nascimento

{"title":"Anomaly Detection Technique for Intrusion Detection in SDN Environment using Continuous Data Stream Machine Learning Algorithms","authors":"A. Ribeiro, R. Santos, A. Nascimento","doi":"10.1109/SysCon48628.2021.9447092","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9447092","url":null,"abstract":"Software Defined Networks (SDN) present some security weakness due to the separation between control and data planes. Thus, some operational security mechanisms have been designed to deal with malicious code in SDN. However, most of those approaches require a signature basis and present the inability to anticipate novel malicious activity. Other anomaly based approaches are inefficient due to the possibility of an attacker simulates legitimate traffic, which causes lots of false alarms. Thus, in this paper, we present an anomaly based approaches that uses machine learning algorithms over continuous data stream for intrusion detection in a SDN environment. Our approach is to overcome the main challenges that happen when developing an anomaly based system using machine learning algorithms. For characterising the anomalies, we have analysed a type of DDoS attack classified as infrastructure attack that considers the impact of both bandwidth and resource depletions. This type of attack imposes a high affect to the whole SDN. In fact, there are two types of attacks. The bandwidth depletion attack targets the channel between the switches and the controller through either UDP or HTTP flooding. Another way to exhaust outgoing and ingoing bandwidths is through ICMP flooding. The resource depletion attack attempts to exhaust the flow table of switches through SYN flooding. From experiments, we notice that the solution obtains 97.83% accuracy, 99% recall, 80% precision and 2.3% FPR for 10% DDoS attacks on the normal traffic. These results show the effectiveness of the proposed technique.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129736007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Performance Quantification and Heart Rate Analysis in A Repeated-trial Simulation-based Training Task 基于重复试验模拟的训练任务中的性能量化和心率分析

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447147

Imali T. Hettiarachchi, Samer Hanoun, R. Veerabhadrappa, D. Jia, S. Hosking, A. Bhatti

{"title":"Performance Quantification and Heart Rate Analysis in A Repeated-trial Simulation-based Training Task","authors":"Imali T. Hettiarachchi, Samer Hanoun, R. Veerabhadrappa, D. Jia, S. Hosking, A. Bhatti","doi":"10.1109/SysCon48628.2021.9447147","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9447147","url":null,"abstract":"In this study, we investigated the relationship between individuals’ performance and their heart rate (HR) in a simulation-based training task. Participants were required to monitor a set of unmanned aerial vehicles (UAVs) simulated on a computer monitor, observe fuel levels and refuel within a defined time window. The task was cognitively demanding as it required individuals to be highly attentive. Participants took part in ten trials while their eye-gaze, HR and galvanic skin response were acquired simultaneously. Based on the retention of task performance in later trials, participants were categorised into two groups, a high performance (HP) group and low performance (LP) group. We found that the HP group showed a higher HR compared to the LP group while performing the task, with a difference of around 7 beats per minute. This finding was verified by participants’ responses to a post experiment feedback questionnaires. HP participants with higher HR reported better cognitive engagement compared to the LP participants. The LP group reported higher task difficulty compared to the HP group, which might have caused them to exert low effort leading to less engagement. This was reflected in their lower HR and performance scores, compared to the HP group. A regression analysis between performance scores and HR also indicated that HR could be used as a predictor of performance between individuals with high task engagement.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124176581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Failure Prediction Model for Large Scale Cloud Applications using Deep Learning 基于深度学习的大规模云应用故障预测模型

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447141

Mohammad S. Jassas, Q. Mahmoud

{"title":"A Failure Prediction Model for Large Scale Cloud Applications using Deep Learning","authors":"Mohammad S. Jassas, Q. Mahmoud","doi":"10.1109/SysCon48628.2021.9447141","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9447141","url":null,"abstract":"Many cloud service providers face significant challenges in preventing hardware and software failure from occurring. Due to the large scale and heterogeneous nature of cloud computing, cloud services continue to experience failures in their components. A significant proportion of previous studies have focused on the characterization of failed jobs and understanding their behavior, while a few studies have focused on failure prediction, with a focus on increasing the accuracy of failure prediction models. This paper presents the development and implementation of a failure prediction model using a deep learning approach. The proposed model can identify and detect failed tasks early on before they occur. The key feature of the failure prediction model is to improve the performance of cloud applications by reducing the number of failed jobs. In order to investigate the behavior of failure and apply the prediction of failure to the large-scale environment, we used three different traces, namely Google Cluster Trace, Mustang and Trinity. Moreover, we have evaluated the proposed model performance using different evaluation metrics to ensure that the proposed model provides the highest accuracy of predicted values. The proposed model is designed and implemented to achieve high accuracy for failure prediction, regardless of whether the model uses a large or small trace size. The evaluation results show that our proposed model achieved a high precision, recall and f1 score.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132402884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A CADDEX tool for CAD and CAE integration 用于CAD和CAE集成的CADDEX工具

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9495827

Ghaith Gandouzi, I. Belhadj, N. Aifaoui, M. Hammadi, J. Choley

{"title":"A CADDEX tool for CAD and CAE integration","authors":"Ghaith Gandouzi, I. Belhadj, N. Aifaoui, M. Hammadi, J. Choley","doi":"10.1109/SysCon48628.2021.9495827","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9495827","url":null,"abstract":"In recent years, the CAD-simulation integration becomes more than important in the design of complex mechanical products. In CAD environment, the simulation incorporates not only the FEM simulation, but also the simulation of assembly and disassembly plans, tolerancing, etc. The mock-up of mechatronic products made by CAD systems must respond to the requirements given by the system engineer. This loop, when automated, ensures the interoperability between different systems of an industrial product. However, in reality, there is a lack of continuity between the CAD designer and the system engineer. Some CAD data are needed to be transferred to Computer Aided Engineering applications (CAE) such as: part’s data (mass, volume, inertia matrix, center of mass coordinates…) and the assembly data (assembly constraint, assembly matrices, dynamic collision, etc.). In this paper, a FreeCAD Workbench application is developed and implemented as an open-source tool. The main objective is to question the CAD system in order to extract relevant data and export them under a standard format. The SysML graphic formalism is used to develop the proposed tool, named CADDEX©. Using the implemented tool, the collected data parts are exported as an XML format which is used by the system engineering (SE). An illustrative example is used to highlight the advantages and limitations of the proposed tool.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134501872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Systems Design for EEG Signal Classification of Sensorimotor Activity Using Machine Learning 基于机器学习的感觉运动脑电信号分类系统设计

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447106

Jacqueline Heaton, S. Givigi

{"title":"Systems Design for EEG Signal Classification of Sensorimotor Activity Using Machine Learning","authors":"Jacqueline Heaton, S. Givigi","doi":"10.1109/SysCon48628.2021.9447106","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9447106","url":null,"abstract":"This paper proposes a systems design for classifying EEG motor movement signals using AI that achieves a high degree of accuracy. EEG motor movement signals are generated by the brain when the subject consciously attempts to move their body. These signals are reflective of the kind of movement they are attempting to achieve, and improving the classification would allow for better assistive devices for the physically disabled. AI classification requires features to be extracted from the raw data. Features can be extracted using different algorithms. The systems design allows the selection of different features. The features used are calculated from the datapoints corresponding to 1 second windows and transformed into the sigma ($Sigma$), phi ($Phi$), and omega ($Omega$) features. To our knowledge, this is the first time that these features have been used with machine learning techniques. The approach allows the use of different classification models. We test the system with a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), which were both trained on these features, and each window classified independently according to the model. The SVM had an average accuracy of 88%, while the neural network had a higher accuracy of 94%. There was a relatively large amount of variance in the accuracy for different subjects, ranging from 45.9% to 99.6% for the SVM and 24.3% to 99.7% for the ANN. The proof of concept demonstrates that different machine learning algorithms can be used for classification if a pipeline architecture is used.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115114788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of Sensor Placement in a Bridge Structural Health Monitoring System 桥梁结构健康监测系统中传感器布置的优化

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447077

Juan C. Avendano, L. D. Otero, C. Otero

{"title":"Optimization of Sensor Placement in a Bridge Structural Health Monitoring System","authors":"Juan C. Avendano, L. D. Otero, C. Otero","doi":"10.1109/SysCon48628.2021.9447077","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9447077","url":null,"abstract":"This paper presents an optimal sensor placement (OSP) technique designed to be implemented on Structural Health Monitoring (SHM) systems. A steel bridge was modeled in ANSYS environment and four load values were applied at pre-identified locations to generate data. Each experiment yielded an array of data that contains the location, as well as corresponding deformation and safety factors. Measurements were taken at 1,000,000 positions on the bridge and a library of a similar number of failure modes was created for each experiment. Each data library was processed as a multi-dimensional matrix by applying the average filtering algorithm. Local extrema were identified in terms of the corresponding deformation and safety factors by removing repeated values at nearby locations. The results provided a list of 100 locations with maximum deformation or minimum safety factors, containing the optimized positions on the bridge for placement of sensors. The final developed system that includes this placement algorithm capable of simulating multiple load conditions on structures, identifying possible failure points, and detecting and predicting failure scenarios. Both hardware and software implementations of a model of a bridge were performed as a pilot project to validate the proposed system.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125840651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Planning of the Coordination of Multiple Quadrotors Applied to the Transport of Materials 多旋翼机在物料运输中的协调规划

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447061

Walber Lima Pinto Junior, Luiz Eugênio Santos Araújo Filho, C. Nascimento, S. Santos, W. C. Cunha

{"title":"Planning of the Coordination of Multiple Quadrotors Applied to the Transport of Materials","authors":"Walber Lima Pinto Junior, Luiz Eugênio Santos Araújo Filho, C. Nascimento, S. Santos, W. C. Cunha","doi":"10.1109/SysCon48628.2021.9447061","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9447061","url":null,"abstract":"The problem of resource allocation is still a large study area, where different techniques are applied to find an optimal or sub-optimal solution to the problem. This work presents a solution to this problem that uses a Reinforcement Learning technique called Learning Automata, in conjunction with the A* heuristic search algorithm, to allocate material transport tasks to multiple agents and calculate routes to perform these tasks. The vehicles used as agents are small quadrotors. The A* algorithm was applied to generate optimal local routes for each carrier and occasionally resolve conflicts between them. Diagonal distance heuristics were used and a small modification was made to the algorithm that avoids convergence to a non-optimal route. A Pure Pursuit path tracking algorithm was used to give velocity commands to the agents in order to follow the path reference given by the A* algorithm. The various analyzed cases of the learning algorithm and a scalability test showed that the proposed solution is capable of finding sub-optimal solutions in a reasonable time for small and medium scale problems, showing that the route plan learned can solve the proposed tasks. The solutions were applied in the Gazebo simulation environment where the communication with the learning algorithm on MATLAB has been done via ROS.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125856840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

diaLogic: Interaction-Focused Speaker Diarization 对话:以互动为中心的演讲者对话

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447101

R. Duke, A. Doboli

{"title":"diaLogic: Interaction-Focused Speaker Diarization","authors":"R. Duke, A. Doboli","doi":"10.1109/SysCon48628.2021.9447101","DOIUrl":"https://doi.org/10.1109/SysCon48628.2021.9447101","url":null,"abstract":"diaLogic is a user-friendly Python program which performs social interaction classification through speaker diarization. The main libraries used include Python’s PyQt5 and Keras APIs, Matplotlib, and the computational R language. Speaker diarization is achieved with high consistency due to a simple four-layer convolutional neural network (CNN) trained on the Librispeech ASR corpus. Speaker interactions are modeled through a custom R language script. The data generated by the program allows the characterization of speaker traits within social experiments. Group leaders, followers, and level of speaker contribution can be characterized. These traits can be used to determine overall group performance, as well as the performance of individuals. The interface is designed to be simplistic and intuitive, which allows easy operation by nonengineers. This design consideration allows program operation with minimal training for users in the social sciences disciplines. The program is designed with a modular backend, which is invisible to the user of the program. The backend allows easy expansion through modular algorithms. For future iterations of the program, speaker interaction data collection will be fully automated through machine learning and/or logical constructs. The integration of voice-based emotion recognition will be the next phase for this program. Overall, the diaLogic program is the central workspace for social interaction characterization.","PeriodicalId":384949,"journal":{"name":"2021 IEEE International Systems Conference (SysCon)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128322213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluating the Use of Technology Readiness Levels (TRLs) for Cybersecurity Systems 评估网络安全系统的技术就绪水平(trl)的使用

2021 IEEE International Systems Conference (SysCon) Pub Date : 2021-04-15 DOI: 10.1109/SysCon48628.2021.9447130

J. Straub

引用次数: 0